Abstract

The aim of this study is to develop a quasi-optimal algorithm for processing complex radar signals for real-time object detection and identification. The algorithm is synthesized using a method for estimating the mutual correlation function between current and reference object images. The synthesis methodology is based on identifying informative signal components and constructing an objective function whose extremum yields the object coordinates and enables their identification. The algorithm performs correlation-based image processing in both detection and continuous tracking modes. In the detection mode, the objective function minimizes the probabilities of missed detection and false alarm, whereas in the tracking mode it minimizes the coordinate estimation error. This is achieved by sequential processing of the complex phase spectra of images along cross-sections of the two-dimensional spectra defined by a finite set of rational angles. Expressions for these angles are derived. A quasi-optimal correlation-extremum algorithm for processing radar images of the Earth’s surface is developed. The transfer functions of the optimal filter are derived. A method for sequential computation of the mutual correlation function between current and reference images using cross-sections of the two-dimensional phase spectrum is proposed. The computational cost reduction coefficient relative to the optimal algorithm is determined.

Keywords

  • Probing Signal
  • Noise
  • Algorithm
  • Correlation Function
  • Objective Function
  • Correlation-Extremum Algorithm
  • Optimal Filter
  • Mathematical Expectation

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